You need a properly sized numpy array, meaning a HxWx3 array with integers. I tested it with the following code and input, seems to work as expected.

import os.path
import numpy as np
from PIL import Image


def pil2numpy(img: Image = None) -> np.ndarray:
    """
    Convert an HxW pixels RGB Image into an HxWx3 numpy ndarray
    """

    if img is None:
        img = Image.open('amsterdam_190x150.jpg'))

    np_array = np.asarray(img)
    return np_array


def numpy2pil(np_array: np.ndarray) -> Image:
    """
    Convert an HxWx3 numpy array into an RGB Image
    """

    assert_msg = 'Input shall be a HxWx3 ndarray'
    assert isinstance(np_array, np.ndarray), assert_msg
    assert len(np_array.shape) == 3, assert_msg
    assert np_array.shape[2] == 3, assert_msg

    img = Image.fromarray(np_array, 'RGB')
    return img


if __name__ == '__main__':
    data = pil2numpy()
    img = numpy2pil(data)
    img.show()

I am using:

• Python 3.6.3
• numpy 1.14.2
• Pillow 4.3.0

In your comment you specify that the red_arr, etc. are arrays of the range -4000 to 4000.

But if we take a look at the specifications of the Image.from_array modes, then we see that it expects a matrix of three bytes (values from zero to 255).

This is however not per se a problem: we can perform:

def rescale(arr):
    arr_min = arr.min()
    arr_max = arr.max()
    return (arr - arr_min) / (arr_max - arr_min)

red_arr_b = 255.0 * rescale(red_arr)
green_arr_b = 255.0 * rescale(green_arr)
blue_arr_b = 255.0 * rescale(blue_arr)

arr[:,:,0] = red_arr_b
arr[:,:,1] = green_arr_b
arr[:,:,2] = blue_arr_b

img = Image.fromarray(arr.astype(int), 'RGB')

So first we rescale to a range 0 to 255, and then we feed that array to the PIL.

It is also possible that we want to scale red, green and blue the same way. In that case we can use:

def rescale(arr):
    arr_min = arr.min()
    arr_max = arr.max()
    return (arr - arr_min) / (arr_max - arr_min)

arr[:,:,0] = red_arr
arr[:,:,1] = green_arr
arr[:,:,2] = blue_arr

arr = 255.0 * rescale(arr)

img = Image.fromarray(arr.astype(int), 'RGB')

It seems to me that there are 3 aspects to your question:

• creating a colormap, or LUT "Lookup Table"
• scaling your pixels to a standard range
• applying a colormap to an image without matplotlib.

So, to create a colormap, we need a list of 256 RGB values in which we can lookup any greyscale value and find the colour we want to show it as. I understand you can't use matplotlib in production, but you can grab a colormap or two from there on some other machine and copy it to your production machine. So, let's do that. We'll take the viridis colormap:

#!/usr/bin/env python3

import numpy as np
from matplotlib import cm

# Get 256 entries from "viridis" or any other Matplotlib colormap
colmap = cm.get_cmap('viridis', 256)

# Make a Numpy array of the 256 RGB values
# Each line corresponds to an RGB colour for a greyscale level
np.savetxt('cmap.csv', (colmap.colors[...,0:3]*255).astype(np.uint8), fmt='%d', delimiter=',')

If we look at that file "cmap.csv", it has 256 lines and starts like this:

68,1,84
68,2,85
68,3,87
69,5,88
69,6,90
69,8,91
70,9,92
...
...

That means anywhere we see 0 in the greyscale image, we actually colour the pixel rgb(68,1,86). Anywhere we see 1 in the greyscale it maps to rgb(68,2,85). Presumably you can copy that file to your production machine and you can choose any one of Matplotlibs colormaps.

Regarding scaling, you have posted an 8-bit PNG image with a range of 0..117 rather than a 16-bit TIFF image with a range of 17..317, so that is not most helpful. Also, you have not said how you plan to read a TIFF on a system that doesn't have OpenCV or matplotlib, so I don't know whether you have PIL/Pillow or plan to use tifffile.

Instead then, I will create a dummy 32x32 image with a range of 17..317 like this:

grey = np.random.randint(17,318, (32,32))

That looks like this:

array([[244,  75, 237, ..., 154, 190,  70],
       [286, 247, 158, ..., 150, 267, 124],
       [170, 305, 237, ..., 126, 111, 236],
       ...,
       [163, 292, 184, ...,  24, 253, 177],
       [314,  34,  36, ...,  87, 316, 182],
       [258, 153, 278, ..., 189,  57, 196]])

If we now want to scale that from the range 17..317 into the range 0..255, we can use:

rescaled = ((grey.astype(float) - grey.min())*255/(grey.max() - grey.min())).astype(np.uint8)

which gives us this:

array([[192,  49, 187, ..., 116, 147,  45],
       [228, 195, 119, ..., 113, 212,  90],
       [130, 244, 187, ...,  92,  79, 186],
       ...,
       [124, 233, 141, ...,   5, 200, 136],
       [252,  14,  16, ...,  59, 254, 140],
       [204, 115, 221, ..., 146,  34, 152]], dtype=uint8) 

Looking up can be done by loading the CSV file containing our colourmap, and taking the corresponding elements from the colormap as indexed by your greyscale values in the range 0...255:

#!/usr/bin/env python3

import numpy as np
from PIL import Image

# Load image as greyscale and make into Numpy array
grey = np.array(Image.open('TdoGc.png').convert('L'))

# Load RGB LUT from CSV file
lut = np.loadtxt('cmap.csv', dtype=np.uint8, delimiter=',')

# Make output image, same height and width as grey image, but 3-channel RGB
result = np.zeros((*grey.shape,3), dtype=np.uint8)

# Take entries from RGB LUT according to greyscale values in image
np.take(lut, grey, axis=0, out=result)

# Save result
Image.fromarray(result).save('result.png')

If you scale your greyscale image to the full range after reading, using this line:

grey = ((grey.astype(float) - grey.min())*255/(grey.max() - grey.min())).astype(np.uint8)

You will get this:

If you want to visualise your colormap, change the line above that looks like this:

grey = np.array(Image.open('TdoGc.png').convert('L'))

into this so that it generates a gradient (ramp) image:

grey = np.repeat(np.arange(256,dtype=np.uint8).reshape(1,-1), 100, axis=0)

Then you can see your colourmap:

A quick hack to handle segmented linear colormaps that don't have a class variable enumerating the colours. So you can make the cmap.csv file from the autumn colourmap like this:

import numpy as np
from matplotlib import cm

# Get "autumn" colourmap
colmap = cm.get_cmap('autumn')

# Save 256 RGB entries as CSV - one for each of grey levels 0..255
np.savetxt('cmap.csv', np.array([colmap(i/255)[:3] for i in range(256)]) * 255, fmt="%d", delimiter=',')

You don't need to convert NumPy array to Mat because OpenCV cv2 module can accept NumPyarray. The only thing you need to care for is that {0,1} is mapped to {0,255} and any value bigger than 1 in NumPy array is equal to 255. So you should divide by 255 in your code, as shown below.

img = numpy.zeros([5,5,3])

img[:,:,0] = numpy.ones([5,5])*64/255.0
img[:,:,1] = numpy.ones([5,5])*128/255.0
img[:,:,2] = numpy.ones([5,5])*192/255.0

cv2.imwrite('color_img.jpg', img)
cv2.imshow("image", img)
cv2.waitKey()

You can save time by saving to a io.BytesIO instead of to a file:

import io
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from PIL import Image

def ax_to_array(ax, **kwargs):
    fig = ax.figure
    frameon = ax.get_frame_on()
    ax.set_frame_on(False)
    with io.BytesIO() as memf:
        extent = ax.get_window_extent()
        extent = extent.transformed(fig.dpi_scale_trans.inverted())
        plt.axis('off')
        fig.savefig(memf, format='PNG', bbox_inches=extent, **kwargs)
        memf.seek(0)
        arr = mpimg.imread(memf)[::-1,...]
    ax.set_frame_on(frameon) 
    return arr.copy()

rand_matrix = np.random.rand(10,10)
fig, ax = plt.subplots()
ax.imshow(rand_matrix)
result = ax_to_array(ax)
# view using matplotlib
plt.show()
# view using PIL
result = (result * 255).astype('uint8')
img = Image.fromarray(result)
img.show()

You can use newer OpenCV python interface (if I'm not mistaken it is available since OpenCV 2.2). It natively uses numpy arrays:

import cv2
im = cv2.imread("abc.tiff")
print(type(im))

result:

<type 'numpy.ndarray'>

rgb = np.dstack((r,g,b))  # stacks 3 h x w arrays -> h x w x 3

To also convert floats 0 .. 1 to uint8 s,

rgb_uint8 = (np.dstack((r,g,b)) * 255.999) .astype(np.uint8)  # right, Janna, not 256

The fourth layer is the transparency value for image formats that support transparency, like PNG. If you remove the 4th value it'll be a correct RGB image without transparency.

EDIT:

Example:

>>> import PIL.Image
>>> image = PIL.Image.open('../test.png')
>>> import numpy as np
>>> image = np.array(image)
>>> image.shape
(381, 538, 4)
>>> image[...,:3].shape
(381, 538, 3)

Quite a busy one-liner, but here it is:

1. First ensure your NumPy array, myarray, is normalised with the max value at 1.0.
2. Apply the colormap directly to myarray.
3. Rescale to the 0-255 range.
4. Convert to integers, using np.uint8().
5. Use Image.fromarray().

And you're done:

from PIL import Image
from matplotlib import cm
im = Image.fromarray(np.uint8(cm.gist_earth(myarray)*255))

with plt.savefig():

with im.save():